Quantitative phase imaging-based machine learning approaches for the analysis of adherent and suspended cells

Björn Kemper; Hanna Eilers; Tilmann Klein; Klaus Brinker; Steffi Ketelhut

doi:10.1117/12.2577825

5 March 2021 Quantitative phase imaging-based machine learning approaches for the analysis of adherent and suspended cells

Björn Kemper, Hanna Eilers, Tilmann Klein, Klaus Brinker, Steffi Ketelhut

Author Affiliations +

Proceedings Volume 11649, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXVIII; 116490B (2021) https://doi.org/10.1117/12.2577825
Event: SPIE BiOS, 2021, Online Only

Abstract

For the example of digital holographic microscopy (DHM) we explored strategies to discriminate adherent and suspended single cells utilizing biophysical parameters retrieved label-free from DHM quantitative phase images in combination with machine learning (ML). Quantitative DHM phase contrast images of adherent cells were segmented while suspended single cells were analyzed based on a two-dimensional fitting approach. The retrieved parameter clouds were subsequently evaluated with different ML algorithms with the aim of an intuitive and user-friendly data representation. The results of the study demonstrate that our approach is capable for reliable discrimination between different cell types and to distinguish between different phenotypes.

Conference Presentation

Citation Download Citation

Björn Kemper, Hanna Eilers, Tilmann Klein, Klaus Brinker, and Steffi Ketelhut "Quantitative phase imaging-based machine learning approaches for the analysis of adherent and suspended cells", Proc. SPIE 11649, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXVIII, 116490B (5 March 2021); https://doi.org/10.1117/12.2577825

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